The Effectiveness of Concept Based Search for Video Retrieval

In this paper we investigate how a small number of high-level concepts\ud derived for video shots, such as Sport. Face.Indoor. etc., can be used effectively for ad hoc search in video material. We will answer the following questions: 1) Can we automatically construct concept queries from ordinary text queries? 2) What is the best way to combine evidence from single concept detectors into final search results? We evaluated algorithms for automatic concept query formulation using WordNet based concept extraction, and we evaluated algorithms for fast, on-line combination of concepts. Experimental results on data from the TREC Video 2005 workshop and 25 test users show the following. 1) Automatic query formulation through WordNet based concept extraction can achieve comparable results to user created query concepts and 2) Combination methods that take neighboring shots into account outperform more simple combination methods

Cloud-Based Retrieval Information System Using Concept for Multi-Format Data

The need of effective and efficient method to retrieving non-Web-enabled and Web-enabled information entities is essential, due to the fact of inaccuracy of the existing search engines that still use traditional term-based indexing for text documents and annotation text for images, audio and video files. Previous works showed that incorporating the knowledge in the form of concepts into an information retrieval system may increase the effectiveness of the retrieving method. Unfortunately, most of the works that implemented the concept-based information retrieval system still focused on one information format. This paper proposes a multi-format (text, image, video and, audio) concept-based information retrieval method for Cloud environment. The proposed method is implemented in a laboratory-scale heterogeneous cloud environment using Eucalyptus middleware.  755 multi-format information is experimented and the performance of the proposed method is measured

Combination of semantic word similarity metrics in video retrieval

Multimedia Information Retrieval is one of the most challenging issues. Search for knowledge in the form of video is the main focus of this study. In recent years, there has been a tremendous need to query and process large amount of video data that cannot be easily described. There is a mismatch between the low-level interpretation of video frames and the way users express their information needs. This issue leads to the problem named semantic gap. To bridge semantic gap, concept-based video retrieval has been considered as a feasible alternative technique for video search. In order to retrieve a desirable video shot, a query should be defined based on users’ needs. In spite of the fact that query can be on object, motion, texture, color and so on, queries which are expressed in terms of semantic concepts are more intuitive and realistic for end users. Therefore, a concept-based video retrieval model based on the combination of the knowledge-based and corpus-based semantic word similarity measures is proposed with respect to bridging semantic gap and supporting semantic queries. In this study, Latent Semantic Analysis (LSA) which is a corpus-based semantic similarity measure is compared to previously utilized corpus-based measures. In addition, we experiment a combination of LSA with a knowledge-based semantic similarity measure in order to improve the retrieval effectiveness. For evaluation purpose, TRECVID 2005 dataset is utilized as well. As experimental results show, combination of knowledge-based and corpus-based outperforms individual one with MAP of 16.29%

Focused image search in the social Web.

Recently, social multimedia-sharing websites, which allow users to upload, annotate, and share online photo or video collections, have become increasingly popular. The user tags or annotations constitute the new multimedia meta-data . We present an image search system that exploits both image textual and visual information. First, we use focused crawling and DOM Tree based web data extraction methods to extract image textual features from social networking image collections. Second, we propose the concept of visual words to handle the image\u27s visual content for fast indexing and searching. We also develop several user friendly search options to allow users to query the index using words and image feature descriptions (visual words). The developed image search system tries to bridge the gap between the scalable industrial image search engines, which are based on keyword search, and the slower content based image retrieval systems developed mostly in the academic field and designed to search based on image content only. We have implemented a working prototype by crawling and indexing over 16,056 images from flickr.com, one of the most popular image sharing websites. Our experimental results on a working prototype confirm the efficiency and effectiveness of the methods, that we proposed

High-level feature detection from video in TRECVid: a 5-year retrospective of achievements

Successful and effective content-based access to digital video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip. The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work done on the TRECVid high-level feature task, showing the progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can achieve large-scale, fast and reliable high-level feature detection on video

Facet-Based Browsing in Video Retrieval: A Simulation-Based Evaluation

In this paper we introduce a novel interactive video retrieval approach which uses sub-needs of an information need for querying and organising the search process. The underlying assumption of this approach is that the search effectiveness will be enhanced when employed for interactive video retrieval. We explore the performance bounds of a faceted system by using the simulated user evaluation methodology on TRECVID data sets and also on the logs of a prior user experiment with the system. We discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. The facets are simulated by the use of clustering the video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness

Strategies for Searching Video Content with Text Queries or Video Examples

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision

Audiovisual archives are investing in large-scale digitisation efforts of their analogue holdings and, in parallel, ingesting an ever-increasing amount of born- digital files in their digital storage facilities. Digitisation opens up new access paradigms and boosted re-use of audiovisual content. Query-log analyses show the shortcomings of manual annotation, therefore archives are complementing these annotations by developing novel search engines that automatically extract information from both audio and the visual tracks. Over the past few years, the TRECVid benchmark has developed a novel relationship with the Netherlands Institute of Sound and Vision (NISV) which goes beyond the NISV just providing data and use cases to TRECVid. Prototype and demonstrator systems developed as part of TRECVid are set to become a key driver in improving the quality of search engines at the NISV and will ultimately help other audiovisual archives to offer more efficient and more fine-grained access to their collections. This paper reports the experiences of NISV in leveraging the activities of the TRECVid benchmark

Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context